Overview

Dataset statistics

Number of variables19
Number of observations5872
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory871.8 KiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 5619 distinct valuesHigh cardinality
artist has a high cardinality: 2920 distinct valuesHigh cardinality
uri has a high cardinality: 5855 distinct valuesHigh cardinality
danceability is highly overall correlated with energy and 2 other fieldsHigh correlation
energy is highly overall correlated with danceability and 4 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
instrumentalness is highly overall correlated with targetHigh correlation
target is highly overall correlated with danceability and 2 other fieldsHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 632 (10.8%) zerosZeros
instrumentalness has 2206 (37.6%) zerosZeros

Reproduction

Analysis started2022-11-29 22:51:27.698967
Analysis finished2022-11-29 22:51:45.549145
Duration17.85 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5619
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
Breathe
 
5
Beautiful
 
4
The One
 
4
Forever
 
4
Closer
 
4
Other values (5614)
5851 

Length

Max length124
Median length95
Mean length17.44346
Min length1

Characters and Unicode

Total characters102428
Distinct characters162
Distinct categories14 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5401 ?
Unique (%)92.0%

Sample

1st rowLucky Man
2nd rowOn The Hotline
3rd rowClouds Of Dementia
4th rowHeavy Metal, Raise Hell!
5th rowI Got A Feelin'

Common Values

ValueCountFrequency (%)
Breathe 5
 
0.1%
Beautiful 4
 
0.1%
The One 4
 
0.1%
Forever 4
 
0.1%
Closer 4
 
0.1%
Girlfriend 4
 
0.1%
Angel 4
 
0.1%
You Raise Me Up 3
 
0.1%
Love Song 3
 
0.1%
Someday 3
 
0.1%
Other values (5609) 5834
99.4%

Length

2022-11-29T17:51:45.649411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 679
 
3.5%
540
 
2.8%
you 352
 
1.8%
i 300
 
1.5%
of 287
 
1.5%
a 249
 
1.3%
me 238
 
1.2%
in 215
 
1.1%
to 193
 
1.0%
love 172
 
0.9%
Other values (5597) 16267
83.5%

Most occurring characters

ValueCountFrequency (%)
13620
 
13.3%
e 9090
 
8.9%
o 6422
 
6.3%
a 6308
 
6.2%
n 5173
 
5.1%
i 4988
 
4.9%
t 4664
 
4.6%
r 4645
 
4.5%
s 3330
 
3.3%
l 3158
 
3.1%
Other values (152) 41030
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66861
65.3%
Uppercase Letter 18107
 
17.7%
Space Separator 13620
 
13.3%
Other Punctuation 1722
 
1.7%
Decimal Number 702
 
0.7%
Dash Punctuation 567
 
0.6%
Open Punctuation 391
 
0.4%
Close Punctuation 390
 
0.4%
Other Letter 40
 
< 0.1%
Final Punctuation 8
 
< 0.1%
Other values (4) 20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9090
13.6%
o 6422
 
9.6%
a 6308
 
9.4%
n 5173
 
7.7%
i 4988
 
7.5%
t 4664
 
7.0%
r 4645
 
6.9%
s 3330
 
5.0%
l 3158
 
4.7%
h 2798
 
4.2%
Other values (37) 16285
24.4%
Other Letter
ValueCountFrequency (%)
3
 
7.5%
2
 
5.0%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
1
 
2.5%
Other values (27) 27
67.5%
Uppercase Letter
ValueCountFrequency (%)
T 1675
 
9.3%
S 1401
 
7.7%
M 1318
 
7.3%
A 1141
 
6.3%
I 1130
 
6.2%
B 1068
 
5.9%
L 991
 
5.5%
W 941
 
5.2%
D 894
 
4.9%
R 856
 
4.7%
Other values (22) 6692
37.0%
Other Punctuation
ValueCountFrequency (%)
' 684
39.7%
. 324
18.8%
, 188
 
10.9%
: 183
 
10.6%
" 94
 
5.5%
& 53
 
3.1%
/ 52
 
3.0%
! 46
 
2.7%
? 35
 
2.0%
; 34
 
2.0%
Other values (6) 29
 
1.7%
Decimal Number
ValueCountFrequency (%)
1 135
19.2%
0 130
18.5%
2 129
18.4%
9 60
8.5%
5 56
8.0%
4 54
 
7.7%
3 44
 
6.3%
7 39
 
5.6%
6 29
 
4.1%
8 26
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 564
99.5%
2
 
0.4%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 378
96.7%
[ 11
 
2.8%
2
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 3
60.0%
= 1
 
20.0%
+ 1
 
20.0%
Nonspacing Mark
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 379
97.2%
] 11
 
2.8%
Final Punctuation
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Modifier Symbol
ValueCountFrequency (%)
´ 4
66.7%
` 2
33.3%
Space Separator
ValueCountFrequency (%)
13620
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84968
83.0%
Common 17416
 
17.0%
Han 19
 
< 0.1%
Thai 17
 
< 0.1%
Hiragana 4
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9090
 
10.7%
o 6422
 
7.6%
a 6308
 
7.4%
n 5173
 
6.1%
i 4988
 
5.9%
t 4664
 
5.5%
r 4645
 
5.5%
s 3330
 
3.9%
l 3158
 
3.7%
h 2798
 
3.3%
Other values (69) 34392
40.5%
Common
ValueCountFrequency (%)
13620
78.2%
' 684
 
3.9%
- 564
 
3.2%
) 379
 
2.2%
( 378
 
2.2%
. 324
 
1.9%
, 188
 
1.1%
: 183
 
1.1%
1 135
 
0.8%
0 130
 
0.7%
Other values (33) 831
 
4.8%
Han
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9) 9
47.4%
Thai
ValueCountFrequency (%)
3
17.6%
2
11.8%
2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (3) 3
17.6%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102212
99.8%
None 158
 
0.2%
CJK 19
 
< 0.1%
Thai 17
 
< 0.1%
Punctuation 14
 
< 0.1%
Hiragana 4
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13620
 
13.3%
e 9090
 
8.9%
o 6422
 
6.3%
a 6308
 
6.2%
n 5173
 
5.1%
i 4988
 
4.9%
t 4664
 
4.6%
r 4645
 
4.5%
s 3330
 
3.3%
l 3158
 
3.1%
Other values (77) 40814
39.9%
None
ValueCountFrequency (%)
é 42
26.6%
á 20
12.7%
ä 15
 
9.5%
ö 9
 
5.7%
å 8
 
5.1%
ó 8
 
5.1%
è 7
 
4.4%
ê 5
 
3.2%
´ 4
 
2.5%
ø 3
 
1.9%
Other values (19) 37
23.4%
Punctuation
ValueCountFrequency (%)
7
50.0%
2
 
14.3%
2
 
14.3%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Thai
ValueCountFrequency (%)
3
17.6%
2
11.8%
2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Other values (3) 3
17.6%
CJK
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9) 9
47.4%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

artist
Categorical

Distinct2920
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
Toby Keith
 
27
Tim McGraw
 
24
Rascal Flatts
 
24
Kenny Chesney
 
23
Iron Maiden
 
23
Other values (2915)
5751 

Length

Max length88
Median length69
Mean length13.979053
Min length2

Characters and Unicode

Total characters82085
Distinct characters98
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1949 ?
Unique (%)33.2%

Sample

1st rowMontgomery Gentry
2nd rowPretty Ricky
3rd rowCandlemass
4th rowZwartketterij
5th rowBilly Currington

Common Values

ValueCountFrequency (%)
Toby Keith 27
 
0.5%
Tim McGraw 24
 
0.4%
Rascal Flatts 24
 
0.4%
Kenny Chesney 23
 
0.4%
Iron Maiden 23
 
0.4%
George Strait 22
 
0.4%
Brad Paisley 20
 
0.3%
Keith Urban 20
 
0.3%
Britney Spears 19
 
0.3%
Alan Jackson 19
 
0.3%
Other values (2910) 5651
96.2%

Length

2022-11-29T17:51:45.780932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
featuring 557
 
4.2%
the 397
 
3.0%
254
 
1.9%
of 117
 
0.9%
lil 99
 
0.7%
keith 56
 
0.4%
wayne 51
 
0.4%
joe 50
 
0.4%
kelly 47
 
0.4%
j 46
 
0.3%
Other values (3673) 11708
87.5%

Most occurring characters

ValueCountFrequency (%)
7510
 
9.1%
e 7133
 
8.7%
a 6849
 
8.3%
n 5123
 
6.2%
i 5112
 
6.2%
r 4709
 
5.7%
o 4132
 
5.0%
t 3495
 
4.3%
l 3299
 
4.0%
s 3113
 
3.8%
Other values (88) 31610
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59337
72.3%
Uppercase Letter 13932
 
17.0%
Space Separator 7510
 
9.1%
Other Punctuation 840
 
1.0%
Decimal Number 260
 
0.3%
Dash Punctuation 184
 
0.2%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Currency Symbol 5
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7133
12.0%
a 6849
11.5%
n 5123
 
8.6%
i 5112
 
8.6%
r 4709
 
7.9%
o 4132
 
7.0%
t 3495
 
5.9%
l 3299
 
5.6%
s 3113
 
5.2%
h 2281
 
3.8%
Other values (32) 14091
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 1106
 
7.9%
T 1101
 
7.9%
B 1037
 
7.4%
M 997
 
7.2%
F 987
 
7.1%
C 900
 
6.5%
A 821
 
5.9%
D 768
 
5.5%
J 747
 
5.4%
L 648
 
4.7%
Other values (20) 4820
34.6%
Decimal Number
ValueCountFrequency (%)
0 51
19.6%
2 51
19.6%
5 40
15.4%
1 36
13.8%
3 34
13.1%
4 21
8.1%
6 10
 
3.8%
7 10
 
3.8%
8 6
 
2.3%
9 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 349
41.5%
& 249
29.6%
' 119
 
14.2%
, 62
 
7.4%
! 31
 
3.7%
" 18
 
2.1%
: 7
 
0.8%
/ 4
 
0.5%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
7510
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73269
89.3%
Common 8816
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7133
 
9.7%
a 6849
 
9.3%
n 5123
 
7.0%
i 5112
 
7.0%
r 4709
 
6.4%
o 4132
 
5.6%
t 3495
 
4.8%
l 3299
 
4.5%
s 3113
 
4.2%
h 2281
 
3.1%
Other values (62) 28023
38.2%
Common
ValueCountFrequency (%)
7510
85.2%
. 349
 
4.0%
& 249
 
2.8%
- 184
 
2.1%
' 119
 
1.3%
, 62
 
0.7%
0 51
 
0.6%
2 51
 
0.6%
5 40
 
0.5%
1 36
 
0.4%
Other values (16) 165
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82002
99.9%
None 82
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7510
 
9.2%
e 7133
 
8.7%
a 6849
 
8.4%
n 5123
 
6.2%
i 5112
 
6.2%
r 4709
 
5.7%
o 4132
 
5.0%
t 3495
 
4.3%
l 3299
 
4.0%
s 3113
 
3.8%
Other values (67) 31527
38.4%
None
ValueCountFrequency (%)
ö 17
20.7%
é 12
14.6%
ó 8
9.8%
á 6
 
7.3%
ú 6
 
7.3%
ü 5
 
6.1%
ä 4
 
4.9%
ø 3
 
3.7%
Á 3
 
3.7%
å 3
 
3.7%
Other values (10) 15
18.3%
Punctuation
ValueCountFrequency (%)
1
100.0%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5855
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
spotify:track:561jH07mF1jHuk7KlaeF0s
 
2
spotify:track:1qHRFZE8qykNXYZadzmi1m
 
2
spotify:track:7uKcScNXuO3MWw6LowBjW1
 
2
spotify:track:1mJ05BN0So26a14iib85aI
 
2
spotify:track:0t9Jd84JnsV8HRMaQzHUom
 
2
Other values (5850)
5862 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters211392
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5838 ?
Unique (%)99.4%

Sample

1st rowspotify:track:4GiXBCUF7H6YfNQsnBRIzl
2nd rowspotify:track:1zyqZONW985Cs4osz9wlsu
3rd rowspotify:track:6cHZf7RbxXCKwEkgAZT4mY
4th rowspotify:track:2IjBPp2vMeX7LggzRN3iSX
5th rowspotify:track:1tF370eYXUcWwkIvaq3IGz

Common Values

ValueCountFrequency (%)
spotify:track:561jH07mF1jHuk7KlaeF0s 2
 
< 0.1%
spotify:track:1qHRFZE8qykNXYZadzmi1m 2
 
< 0.1%
spotify:track:7uKcScNXuO3MWw6LowBjW1 2
 
< 0.1%
spotify:track:1mJ05BN0So26a14iib85aI 2
 
< 0.1%
spotify:track:0t9Jd84JnsV8HRMaQzHUom 2
 
< 0.1%
spotify:track:2aIdVb8v9KTpEZnftkz2mD 2
 
< 0.1%
spotify:track:7Kpqjspw4Y7HrvItIRcBiW 2
 
< 0.1%
spotify:track:77FULy278MulVvGWS8BfK7 2
 
< 0.1%
spotify:track:4TbNLKRLKlxZDlS0pu7Lsy 2
 
< 0.1%
spotify:track:3XVBdLihbNbxUwZosxcGuJ 2
 
< 0.1%
Other values (5845) 5852
99.7%

Length

2022-11-29T17:51:45.876042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:561jh07mf1jhuk7klaef0s 2
 
< 0.1%
spotify:track:3xvbdlihbnbxuwzosxcguj 2
 
< 0.1%
spotify:track:1qhrfze8qyknxyzadzmi1m 2
 
< 0.1%
spotify:track:49bujjrc16ngnrggs75yan 2
 
< 0.1%
spotify:track:6pwzcktrkrwbupzy8rlcop 2
 
< 0.1%
spotify:track:5ysw99iuaiiaj243jf7pbo 2
 
< 0.1%
spotify:track:3f3omu8n47mqyab5ncagyt 2
 
< 0.1%
spotify:track:0uhnzk5zi46irlq04lnotc 2
 
< 0.1%
spotify:track:6nvrxjfykkt2spiralmsjh 2
 
< 0.1%
spotify:track:4ggyigsxhpypsgittwlwlt 2
 
< 0.1%
Other values (5845) 5852
99.7%

Most occurring characters

ValueCountFrequency (%)
t 13729
 
6.5%
: 11744
 
5.6%
i 7927
 
3.7%
a 7854
 
3.7%
o 7852
 
3.7%
s 7847
 
3.7%
k 7830
 
3.7%
c 7827
 
3.7%
f 7817
 
3.7%
p 7808
 
3.7%
Other values (53) 123157
58.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122051
57.7%
Uppercase Letter 51715
24.5%
Decimal Number 25882
 
12.2%
Other Punctuation 11744
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 13729
 
11.2%
i 7927
 
6.5%
a 7854
 
6.4%
o 7852
 
6.4%
s 7847
 
6.4%
k 7830
 
6.4%
c 7827
 
6.4%
f 7817
 
6.4%
p 7808
 
6.4%
y 7808
 
6.4%
Other values (16) 37752
30.9%
Uppercase Letter
ValueCountFrequency (%)
A 2067
 
4.0%
S 2066
 
4.0%
V 2052
 
4.0%
B 2028
 
3.9%
D 2027
 
3.9%
N 2026
 
3.9%
U 2017
 
3.9%
F 2017
 
3.9%
E 2016
 
3.9%
T 2012
 
3.9%
Other values (16) 31387
60.7%
Decimal Number
ValueCountFrequency (%)
6 2839
11.0%
4 2808
10.8%
0 2766
10.7%
3 2747
10.6%
1 2733
10.6%
5 2727
10.5%
2 2696
10.4%
7 2646
10.2%
8 1999
7.7%
9 1921
7.4%
Other Punctuation
ValueCountFrequency (%)
: 11744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 173766
82.2%
Common 37626
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 13729
 
7.9%
i 7927
 
4.6%
a 7854
 
4.5%
o 7852
 
4.5%
s 7847
 
4.5%
k 7830
 
4.5%
c 7827
 
4.5%
f 7817
 
4.5%
p 7808
 
4.5%
y 7808
 
4.5%
Other values (42) 89467
51.5%
Common
ValueCountFrequency (%)
: 11744
31.2%
6 2839
 
7.5%
4 2808
 
7.5%
0 2766
 
7.4%
3 2747
 
7.3%
1 2733
 
7.3%
5 2727
 
7.2%
2 2696
 
7.2%
7 2646
 
7.0%
8 1999
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 13729
 
6.5%
: 11744
 
5.6%
i 7927
 
3.7%
a 7854
 
3.7%
o 7852
 
3.7%
s 7847
 
3.7%
k 7830
 
3.7%
c 7827
 
3.7%
f 7817
 
3.7%
p 7808
 
3.7%
Other values (53) 123157
58.3%

danceability
Real number (ℝ)

Distinct881
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54293077
Minimum0.0588
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:45.985337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0588
5-th percentile0.199
Q10.416
median0.556
Q30.681
95-th percentile0.83845
Maximum0.986
Range0.9272
Interquartile range (IQR)0.265

Descriptive statistics

Standard deviation0.19000271
Coefficient of variation (CV)0.34995752
Kurtosis-0.50041185
Mean0.54293077
Median Absolute Deviation (MAD)0.131
Skewness-0.25056872
Sum3188.0895
Variance0.036101029
MonotonicityNot monotonic
2022-11-29T17:51:46.097008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.568 23
 
0.4%
0.499 20
 
0.3%
0.51 20
 
0.3%
0.7 19
 
0.3%
0.549 19
 
0.3%
0.647 19
 
0.3%
0.599 19
 
0.3%
0.557 19
 
0.3%
0.648 19
 
0.3%
0.58 19
 
0.3%
Other values (871) 5676
96.7%
ValueCountFrequency (%)
0.0588 1
< 0.1%
0.0597 1
< 0.1%
0.06 1
< 0.1%
0.0609 1
< 0.1%
0.061 1
< 0.1%
0.064 1
< 0.1%
0.065 1
< 0.1%
0.0655 1
< 0.1%
0.0684 1
< 0.1%
0.0698 1
< 0.1%
ValueCountFrequency (%)
0.986 1
< 0.1%
0.978 1
< 0.1%
0.974 1
< 0.1%
0.968 1
< 0.1%
0.967 1
< 0.1%
0.963 1
< 0.1%
0.962 1
< 0.1%
0.961 1
< 0.1%
0.956 1
< 0.1%
0.955 1
< 0.1%

energy
Real number (ℝ)

Distinct1010
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69451085
Minimum0.000348
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:46.200149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000348
5-th percentile0.17665
Q10.567
median0.744
Q30.885
95-th percentile0.976
Maximum0.999
Range0.998652
Interquartile range (IQR)0.318

Descriptive statistics

Standard deviation0.23779179
Coefficient of variation (CV)0.34238744
Kurtosis0.3946399
Mean0.69451085
Median Absolute Deviation (MAD)0.155
Skewness-0.97812949
Sum4078.1677
Variance0.056544937
MonotonicityNot monotonic
2022-11-29T17:51:46.311963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.913 23
 
0.4%
0.9 22
 
0.4%
0.947 22
 
0.4%
0.959 21
 
0.4%
0.94 20
 
0.3%
0.951 20
 
0.3%
0.936 20
 
0.3%
0.934 20
 
0.3%
0.987 20
 
0.3%
0.871 19
 
0.3%
Other values (1000) 5665
96.5%
ValueCountFrequency (%)
0.000348 1
< 0.1%
0.000982 1
< 0.1%
0.0011 1
< 0.1%
0.00131 1
< 0.1%
0.0015 1
< 0.1%
0.00183 1
< 0.1%
0.00261 1
< 0.1%
0.00267 1
< 0.1%
0.00281 1
< 0.1%
0.00283 1
< 0.1%
ValueCountFrequency (%)
0.999 11
0.2%
0.998 8
0.1%
0.997 9
0.2%
0.996 13
0.2%
0.995 17
0.3%
0.994 14
0.2%
0.993 13
0.2%
0.992 9
0.2%
0.991 14
0.2%
0.99 7
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2763965
Minimum0
Maximum11
Zeros632
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:46.411135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.564153
Coefficient of variation (CV)0.67548999
Kurtosis-1.2950419
Mean5.2763965
Median Absolute Deviation (MAD)3
Skewness0.016527317
Sum30983
Variance12.703187
MonotonicityNot monotonic
2022-11-29T17:51:46.492137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 637
10.8%
0 632
10.8%
1 623
10.6%
2 620
10.6%
9 558
9.5%
4 479
8.2%
11 470
8.0%
5 455
7.7%
6 414
7.1%
8 407
6.9%
Other values (2) 577
9.8%
ValueCountFrequency (%)
0 632
10.8%
1 623
10.6%
2 620
10.6%
3 176
 
3.0%
4 479
8.2%
5 455
7.7%
6 414
7.1%
7 637
10.8%
8 407
6.9%
9 558
9.5%
ValueCountFrequency (%)
11 470
8.0%
10 401
6.8%
9 558
9.5%
8 407
6.9%
7 637
10.8%
6 414
7.1%
5 455
7.7%
4 479
8.2%
3 176
 
3.0%
2 620
10.6%

loudness
Real number (ℝ)

Distinct4420
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.449258
Minimum-47.327
Maximum1.137
Zeros0
Zeros (%)0.0%
Negative5871
Negative (%)> 99.9%
Memory size46.0 KiB
2022-11-29T17:51:46.584865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-47.327
5-th percentile-18.06465
Q1-8.315
median-6.0415
Q3-4.5625
95-th percentile-2.9922
Maximum1.137
Range48.464
Interquartile range (IQR)3.7525

Descriptive statistics

Standard deviation5.1025433
Coefficient of variation (CV)-0.68497336
Kurtosis9.8726338
Mean-7.449258
Median Absolute Deviation (MAD)1.7365
Skewness-2.7491463
Sum-43742.043
Variance26.035948
MonotonicityNot monotonic
2022-11-29T17:51:46.685147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.908 5
 
0.1%
-5.379 5
 
0.1%
-8.527 5
 
0.1%
-4.753 5
 
0.1%
-4.072 5
 
0.1%
-4.549 5
 
0.1%
-4.615 5
 
0.1%
-5.218 5
 
0.1%
-3.798 5
 
0.1%
-5.367 4
 
0.1%
Other values (4410) 5823
99.2%
ValueCountFrequency (%)
-47.327 1
< 0.1%
-44.347 1
< 0.1%
-43.178 1
< 0.1%
-41.086 1
< 0.1%
-39.985 1
< 0.1%
-39.982 1
< 0.1%
-39.779 1
< 0.1%
-39.614 1
< 0.1%
-38.999 1
< 0.1%
-38.917 1
< 0.1%
ValueCountFrequency (%)
1.137 1
< 0.1%
-0.296 1
< 0.1%
-0.366 1
< 0.1%
-0.559 1
< 0.1%
-0.794 1
< 0.1%
-0.864 1
< 0.1%
-0.873 1
< 0.1%
-0.884 1
< 0.1%
-0.949 1
< 0.1%
-0.956 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
1
3788 
0
2084 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

Length

2022-11-29T17:51:46.769509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:46.855396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

Most occurring characters

ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3788
64.5%
0 2084
35.5%

speechiness
Real number (ℝ)

Distinct1077
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092359537
Minimum0.0224
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:47.160880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0224
5-th percentile0.0277
Q10.036175
median0.0527
Q30.107
95-th percentile0.302
Maximum0.95
Range0.9276
Interquartile range (IQR)0.070825

Descriptive statistics

Standard deviation0.094997231
Coefficient of variation (CV)1.028559
Kurtosis10.91819
Mean0.092359537
Median Absolute Deviation (MAD)0.0212
Skewness2.7445916
Sum542.3352
Variance0.0090244739
MonotonicityNot monotonic
2022-11-29T17:51:47.262635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0317 23
 
0.4%
0.0336 23
 
0.4%
0.107 22
 
0.4%
0.0334 21
 
0.4%
0.0316 21
 
0.4%
0.029 21
 
0.4%
0.102 21
 
0.4%
0.0337 21
 
0.4%
0.0348 20
 
0.3%
0.12 20
 
0.3%
Other values (1067) 5659
96.4%
ValueCountFrequency (%)
0.0224 1
 
< 0.1%
0.0227 1
 
< 0.1%
0.0228 1
 
< 0.1%
0.0229 2
< 0.1%
0.0232 4
0.1%
0.0233 2
< 0.1%
0.0234 1
 
< 0.1%
0.0235 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.0237 1
 
< 0.1%
ValueCountFrequency (%)
0.95 1
< 0.1%
0.943 1
< 0.1%
0.941 1
< 0.1%
0.914 1
< 0.1%
0.856 1
< 0.1%
0.822 1
< 0.1%
0.786 1
< 0.1%
0.766 1
< 0.1%
0.758 1
< 0.1%
0.7 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2725
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21437361
Minimum0
Maximum0.996
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:47.382125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1855 × 10-5
Q10.0045525
median0.0603
Q30.312
95-th percentile0.942
Maximum0.996
Range0.996
Interquartile range (IQR)0.3074475

Descriptive statistics

Standard deviation0.29651074
Coefficient of variation (CV)1.3831494
Kurtosis0.81796235
Mean0.21437361
Median Absolute Deviation (MAD)0.0601065
Skewness1.4441476
Sum1258.8018
Variance0.087918617
MonotonicityNot monotonic
2022-11-29T17:51:47.495203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 14
 
0.2%
0.995 13
 
0.2%
0.99 13
 
0.2%
0.991 13
 
0.2%
0.103 13
 
0.2%
0.0108 12
 
0.2%
0.216 12
 
0.2%
0.994 12
 
0.2%
0.989 12
 
0.2%
0.117 11
 
0.2%
Other values (2715) 5747
97.9%
ValueCountFrequency (%)
0 2
< 0.1%
1.01 × 10-61
< 0.1%
1.04 × 10-61
< 0.1%
1.05 × 10-61
< 0.1%
1.06 × 10-61
< 0.1%
1.08 × 10-61
< 0.1%
1.11 × 10-61
< 0.1%
1.12 × 10-61
< 0.1%
1.13 × 10-61
< 0.1%
1.14 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 6
0.1%
0.995 13
0.2%
0.994 12
0.2%
0.993 10
0.2%
0.992 14
0.2%
0.991 13
0.2%
0.99 13
0.2%
0.989 12
0.2%
0.988 6
0.1%
0.987 11
0.2%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2296
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15091715
Minimum0
Maximum0.998
Zeros2206
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:47.596311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.185 × 10-5
Q30.047275
95-th percentile0.893
Maximum0.998
Range0.998
Interquartile range (IQR)0.047275

Descriptive statistics

Standard deviation0.30145183
Coefficient of variation (CV)1.9974656
Kurtosis1.4190822
Mean0.15091715
Median Absolute Deviation (MAD)2.185 × 10-5
Skewness1.7661495
Sum886.18553
Variance0.090873205
MonotonicityNot monotonic
2022-11-29T17:51:47.697853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2206
37.6%
0.826 10
 
0.2%
0.914 9
 
0.2%
0.925 9
 
0.2%
0.883 9
 
0.2%
0.91 8
 
0.1%
0.893 8
 
0.1%
0.877 8
 
0.1%
0.899 7
 
0.1%
0.917 7
 
0.1%
Other values (2286) 3591
61.2%
ValueCountFrequency (%)
0 2206
37.6%
1.01 × 10-63
 
0.1%
1.02 × 10-64
 
0.1%
1.03 × 10-61
 
< 0.1%
1.04 × 10-61
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.08 × 10-63
 
0.1%
1.09 × 10-62
 
< 0.1%
1.1 × 10-63
 
0.1%
ValueCountFrequency (%)
0.998 2
< 0.1%
0.989 2
< 0.1%
0.988 1
< 0.1%
0.985 1
< 0.1%
0.983 2
< 0.1%
0.982 2
< 0.1%
0.981 1
< 0.1%
0.979 2
< 0.1%
0.978 1
< 0.1%
0.976 1
< 0.1%

liveness
Real number (ℝ)

Distinct1200
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1961408
Minimum0.0193
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:47.809212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0193
5-th percentile0.0554
Q10.0937
median0.131
Q30.263
95-th percentile0.53845
Maximum0.987
Range0.9677
Interquartile range (IQR)0.1693

Descriptive statistics

Standard deviation0.16196494
Coefficient of variation (CV)0.82575853
Kurtosis5.2551353
Mean0.1961408
Median Absolute Deviation (MAD)0.054
Skewness2.1147262
Sum1151.7388
Variance0.026232642
MonotonicityNot monotonic
2022-11-29T17:51:47.920380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107 63
 
1.1%
0.101 59
 
1.0%
0.109 55
 
0.9%
0.111 54
 
0.9%
0.108 54
 
0.9%
0.106 53
 
0.9%
0.104 51
 
0.9%
0.105 47
 
0.8%
0.114 46
 
0.8%
0.113 46
 
0.8%
Other values (1190) 5344
91.0%
ValueCountFrequency (%)
0.0193 1
< 0.1%
0.0209 1
< 0.1%
0.0214 1
< 0.1%
0.0216 1
< 0.1%
0.0224 1
< 0.1%
0.0233 1
< 0.1%
0.0234 2
< 0.1%
0.0235 1
< 0.1%
0.0245 1
< 0.1%
0.0246 1
< 0.1%
ValueCountFrequency (%)
0.987 1
< 0.1%
0.985 1
< 0.1%
0.982 1
< 0.1%
0.976 1
< 0.1%
0.974 1
< 0.1%
0.973 1
< 0.1%
0.972 1
< 0.1%
0.971 1
< 0.1%
0.965 2
< 0.1%
0.964 2
< 0.1%

valence
Real number (ℝ)

Distinct1158
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48226214
Minimum0
Maximum0.982
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:48.031681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.064055
Q10.278
median0.486
Q30.687
95-th percentile0.901
Maximum0.982
Range0.982
Interquartile range (IQR)0.409

Descriptive statistics

Standard deviation0.25456689
Coefficient of variation (CV)0.52785999
Kurtosis-0.99245147
Mean0.48226214
Median Absolute Deviation (MAD)0.204
Skewness0.017338509
Sum2831.8433
Variance0.064804301
MonotonicityNot monotonic
2022-11-29T17:51:48.133207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 18
 
0.3%
0.962 17
 
0.3%
0.455 15
 
0.3%
0.502 15
 
0.3%
0.543 15
 
0.3%
0.675 14
 
0.2%
0.504 14
 
0.2%
0.454 14
 
0.2%
0.611 14
 
0.2%
0.391 14
 
0.2%
Other values (1148) 5722
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.0113 1
< 0.1%
0.0172 1
< 0.1%
0.0193 1
< 0.1%
0.0207 2
< 0.1%
0.0209 1
< 0.1%
0.0238 1
< 0.1%
0.0241 1
< 0.1%
0.0246 2
< 0.1%
0.0247 1
< 0.1%
ValueCountFrequency (%)
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%
0.976 1
 
< 0.1%
0.973 1
 
< 0.1%
0.972 3
 
0.1%
0.971 2
 
< 0.1%
0.97 3
 
0.1%
0.969 7
0.1%
0.967 11
0.2%

tempo
Real number (ℝ)

Distinct5445
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.61302
Minimum46.755
Maximum213.233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:48.234813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum46.755
5-th percentile78.1071
Q196.984
median119.999
Q3141.49525
95-th percentile175.72925
Maximum213.233
Range166.478
Interquartile range (IQR)44.51125

Descriptive statistics

Standard deviation30.179885
Coefficient of variation (CV)0.24816328
Kurtosis-0.52201858
Mean121.61302
Median Absolute Deviation (MAD)22.444
Skewness0.38023705
Sum714111.64
Variance910.82548
MonotonicityNot monotonic
2022-11-29T17:51:48.326336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.999 5
 
0.1%
119.987 5
 
0.1%
99.971 4
 
0.1%
109.998 4
 
0.1%
94.997 4
 
0.1%
99.985 4
 
0.1%
125.04 4
 
0.1%
119.988 4
 
0.1%
130.022 4
 
0.1%
95 3
 
0.1%
Other values (5435) 5831
99.3%
ValueCountFrequency (%)
46.755 1
< 0.1%
47.37 1
< 0.1%
49.875 1
< 0.1%
56.028 1
< 0.1%
56.79 1
< 0.1%
58.099 1
< 0.1%
58.5 1
< 0.1%
59.32 1
< 0.1%
59.359 1
< 0.1%
59.972 1
< 0.1%
ValueCountFrequency (%)
213.233 1
< 0.1%
210.857 1
< 0.1%
209.905 1
< 0.1%
209.819 1
< 0.1%
208.078 1
< 0.1%
207.676 1
< 0.1%
207.673 1
< 0.1%
207.575 1
< 0.1%
207.021 1
< 0.1%
206.309 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct4928
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258170.63
Minimum15920
Maximum4170227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:48.428089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15920
5-th percentile145500.7
Q1206813
median238006.5
Q3279160
95-th percentile417653.45
Maximum4170227
Range4154307
Interquartile range (IQR)72347

Descriptive statistics

Standard deviation139534.12
Coefficient of variation (CV)0.54047249
Kurtosis264.80093
Mean258170.63
Median Absolute Deviation (MAD)34760
Skewness11.883994
Sum1.5159779 × 109
Variance1.9469771 × 1010
MonotonicityNot monotonic
2022-11-29T17:51:48.540035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243867 5
 
0.1%
236600 5
 
0.1%
232533 5
 
0.1%
213800 5
 
0.1%
195280 5
 
0.1%
293053 4
 
0.1%
209333 4
 
0.1%
208600 4
 
0.1%
215867 4
 
0.1%
247600 4
 
0.1%
Other values (4918) 5827
99.2%
ValueCountFrequency (%)
15920 1
< 0.1%
23133 1
< 0.1%
24107 1
< 0.1%
25880 1
< 0.1%
27533 1
< 0.1%
28920 1
< 0.1%
31560 1
< 0.1%
31787 1
< 0.1%
34560 1
< 0.1%
38348 1
< 0.1%
ValueCountFrequency (%)
4170227 1
< 0.1%
3816373 1
< 0.1%
3791480 1
< 0.1%
2104347 1
< 0.1%
1761107 1
< 0.1%
1661120 1
< 0.1%
1555093 1
< 0.1%
1514000 1
< 0.1%
1369773 1
< 0.1%
1369173 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
4
5308 
3
 
426
5
 
84
1
 
53
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

Length

2022-11-29T17:51:48.640971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:48.712022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5308
90.4%
3 426
 
7.3%
5 84
 
1.4%
1 53
 
0.9%
0 1
 
< 0.1%

chorus_hit
Real number (ℝ)

Distinct5821
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.734295
Minimum0
Maximum262.6154
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:48.803184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.703187
Q127.507507
median36.03716
Q347.88957
95-th percentile77.826186
Maximum262.6154
Range262.6154
Interquartile range (IQR)20.382062

Descriptive statistics

Standard deviation20.245637
Coefficient of variation (CV)0.49701699
Kurtosis9.9264565
Mean40.734295
Median Absolute Deviation (MAD)9.711455
Skewness2.268137
Sum239191.78
Variance409.88581
MonotonicityNot monotonic
2022-11-29T17:51:48.902576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.2%
28.80315 2
 
< 0.1%
23.25917 2
 
< 0.1%
65.05711 2
 
< 0.1%
71.13102 2
 
< 0.1%
28.35802 2
 
< 0.1%
37.91539 2
 
< 0.1%
26.47049 2
 
< 0.1%
37.18808 2
 
< 0.1%
31.29151 2
 
< 0.1%
Other values (5811) 5840
99.5%
ValueCountFrequency (%)
0 14
0.2%
4.98552 1
 
< 0.1%
6.73588 1
 
< 0.1%
7.1136 1
 
< 0.1%
7.67978 1
 
< 0.1%
7.80022 1
 
< 0.1%
7.9103 1
 
< 0.1%
7.92042 1
 
< 0.1%
8.36681 1
 
< 0.1%
8.98063 1
 
< 0.1%
ValueCountFrequency (%)
262.6154 1
< 0.1%
219.63624 1
< 0.1%
206.9273 1
< 0.1%
187.56901 1
< 0.1%
181.37783 1
< 0.1%
181.27614 1
< 0.1%
180.25775 1
< 0.1%
168.81306 1
< 0.1%
167.82006 1
< 0.1%
166.1522 1
< 0.1%

sections
Real number (ℝ)

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.05688
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.0 KiB
2022-11-29T17:51:48.995976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q312
95-th percentile17
Maximum169
Range168
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.7581857
Coefficient of variation (CV)0.52077853
Kurtosis240.02723
Mean11.05688
Median Absolute Deviation (MAD)2
Skewness11.062967
Sum64926
Variance33.156703
MonotonicityNot monotonic
2022-11-29T17:51:49.097291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 882
15.0%
9 808
13.8%
11 744
12.7%
12 645
11.0%
8 563
9.6%
13 433
7.4%
7 370
6.3%
14 282
 
4.8%
6 211
 
3.6%
15 188
 
3.2%
Other values (48) 746
12.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 13
 
0.2%
3 37
 
0.6%
4 61
 
1.0%
5 124
 
2.1%
6 211
 
3.6%
7 370
6.3%
8 563
9.6%
9 808
13.8%
10 882
15.0%
ValueCountFrequency (%)
169 1
< 0.1%
159 1
< 0.1%
145 1
< 0.1%
97 1
< 0.1%
71 1
< 0.1%
69 1
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
57 2
< 0.1%
56 2
< 0.1%

target
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
1
2936 
0
2936 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5872
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Length

2022-11-29T17:51:49.188616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:49.259573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Most occurring characters

ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5872
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2936
50.0%
0 2936
50.0%

Interactions

2022-11-29T17:51:43.795849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.077097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.215884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.365805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.452917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.195139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.282318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.416262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.672487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.775379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.978709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.128834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.539378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.897126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.165941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.309646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.450469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.537609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.279810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.366977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.501106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.757146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.860051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.063390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.213511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.639674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.998042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.248071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.394291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.535145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.622234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.364482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.451661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.585622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.841741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.960308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.148049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.307470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.724360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.099174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.342156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.478930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.619804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.716007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.449424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.536314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.670110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.919877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.044943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.226201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.398104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.824622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.189891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.431477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.563259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.697949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.785056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.533720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.630094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.739145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.004562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.145222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.310869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.713153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.924891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.280762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.525198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.647938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.782603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.869741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.611861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.714751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.839435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.089241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.245502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.395693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.794747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.009542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.389598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.604166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.732577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.867242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.954054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.696345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.799417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.929514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.173910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.345783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.480518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.886200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.109814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.499873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.692317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.833249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.951916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:33.678252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.780981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.895284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.017777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.258594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.446069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.580649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.977000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.194469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.607241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.776965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.917838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.036593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:33.761381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.865549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.968676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.255684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.343237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.546361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.665491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.070839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.294754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.700028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.861632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.995976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.121224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:33.842180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.950058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.068956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.340323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.434731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.640125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.749996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.150408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.394725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.815941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:29.946273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.096260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.199366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:33.931578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.028198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.153618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.418477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.505757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.724772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.843773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.238558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.510630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:44.916207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.046792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.196313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.284026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.022593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.112842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.237838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.503147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.606069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.809417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:40.950560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.338843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.595294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:45.032132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:30.115605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:31.281166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:32.368671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:34.110909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:35.197484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:36.331599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:37.587813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:38.690887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:39.894066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:41.029033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:42.454731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:43.695585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:51:49.330931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:51:49.483395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:51:49.646136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:51:49.798189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:51:49.940632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:51:50.031828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:51:45.201427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:51:45.433233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Lucky ManMontgomery Gentryspotify:track:4GiXBCUF7H6YfNQsnBRIzl0.5780.4714-7.27010.02890.3680000.000000.15900.532133.061196707430.88059131
1On The HotlinePretty Rickyspotify:track:1zyqZONW985Cs4osz9wlsu0.7040.85410-5.47700.18300.0185000.000000.14800.68892.988242587441.51106101
2Clouds Of DementiaCandlemassspotify:track:6cHZf7RbxXCKwEkgAZT4mY0.1620.8369-3.00910.04730.0001110.004570.17400.30086.964338893465.32887130
3Heavy Metal, Raise Hell!Zwartketterijspotify:track:2IjBPp2vMeX7LggzRN3iSX0.1880.9944-3.74510.16600.0000070.078400.19200.333148.440255667458.5952890
4I Got A Feelin'Billy Curringtonspotify:track:1tF370eYXUcWwkIvaq3IGz0.6300.7642-4.35310.02750.3630000.000000.12500.631112.098193760422.62384101
5Dantzig StationState Of Artspotify:track:5Z3nrC0JbJmXaOGiXTuNFk0.7260.83711-7.22300.09650.3730000.268000.13600.969135.347192720428.29051100
6DivorcedBlacklistedspotify:track:0iAdSLiQBIizTAiLUP7p5E0.3650.9221-2.64410.07100.0028500.000000.32100.29077.25089427445.7720240
7Where I Come FromAlan Jacksonspotify:track:6ej1QJ8eIYmhsyTlvgDajy0.7260.63111-8.13600.03340.2200000.000000.19300.746124.711239240435.59732101
8Nothin' To Die ForTim McGrawspotify:track:3lRSz6HujrSy9b3LXg2Kq90.4810.78610-5.65410.02880.0538000.000000.07590.389153.105253640419.65701111
9I Want to Know Your PlansSay Anythingspotify:track:3pjnCLIHbRczUjenWOEo560.6470.3247-9.67910.03770.3540000.000000.11500.344124.213314286332.66343160
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
5862Postcards From HellThe Wood Brothersspotify:track:72i7dwVrHdfDnr3qmINh5U0.4650.24209-10.46010.02980.9550000.0223000.12600.315102.812284862435.07251140
5863(You Drive Me) CrazyBritney Spearsspotify:track:1DSJNBNhGZCigg9ll5VeZv0.7480.93900-4.28800.03410.0534000.0000000.32000.960104.001198067419.2942691
5864The AnthemGood Charlottespotify:track:0BRHnOFm6sjxN1i9LJrUDu0.4940.93901-3.12710.12600.0066600.0000000.13900.893177.751175093415.89251111
5865I CanNasspotify:track:2NPxL1QqPrD1a7OLHjVcAP0.8370.88506-3.91400.18200.1030000.0000000.06660.69495.313253720417.67790121
5866Shindo-kakuASIAN KUNG-FU GENERATIONspotify:track:1lzVhHihby5uHDwml2ApDr0.3230.95309-4.27810.06170.0000160.3770000.05240.576184.884147200421.9897580
5867Summer RainCarl Thomasspotify:track:0NBHHa8wwwmBnn3aAzX5wJ0.6670.62706-10.48800.06540.0972000.0000520.11100.784186.081232560440.87045101
5868And ICiaraspotify:track:1Jp9n1uHB72CfK31j4mEPh0.6910.38906-10.12510.06530.2550000.0000000.09810.437122.219233840481.7773571
5869Mass in B minor BWV 232, Missa: Duetto - Christe eleison - soprano/mezzo sopranoJohann Sebastian Bachspotify:track:4NIOi1ImMfdufRTsgoKjbD0.2970.07732-23.83910.06200.9510000.0002170.12100.40175.916275560437.51903110
5870LoogThe Cleanspotify:track:2Qyj2nUdm8y37TCCzDasFn0.3900.60107-8.23600.02910.0313000.9470000.11900.439116.122223627439.84092110
5871What The World NeedsWynonnaspotify:track:38Q6YF0TO7E4Dq6K0zdVUk0.5390.74000-5.56600.04900.1940000.0000000.07600.675170.054217160424.95471131